Upload generator.py

generator.py

@@ -0,0 +1,48 @@
+import numpy as np
+
+import torch.nn as nn
+import torch
+
+
+class Generator(nn.Module):
+    def __init__(
+        self,
+        image_shape: (int, int, int),
+        latent_space_dimension: int,
+        use_cuda: bool = False,
+        saved_model: str or None = None
+    ):
+        super(Generator, self).__init__()
+
+        self.image_shape = image_shape
+
+        def block(in_feat, out_feat, normalize=True):
+            layers = [nn.Linear(in_feat, out_feat)]
+            if normalize:
+                layers.append(nn.BatchNorm1d(out_feat, 0.8))
+            layers.append(nn.LeakyReLU(0.2, inplace=True))
+            return layers
+
+        self.model = nn.Sequential(
+            *block(latent_space_dimension, 128, normalize=False),
+            *block(128, 256),
+            *block(256, 512),
+            *block(512, 1024),
+            nn.Linear(1024, int(np.prod(image_shape))),
+            nn.Tanh()
+        )
+        if saved_model is not None:
+            self.model.load_state_dict(
+                torch.load(
+                    saved_model,
+                    map_location=torch.device('cuda' if use_cuda else 'cpu')
+                )
+            )
+
+    def forward(self, z):
+        img = self.model(z)
+        img = img.view(img.shape[0], *self.image_shape)
+        return img
+
+    def save(self, to):
+        torch.save(self.model.state_dict(), to)